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Counting Linear Extensions in Practice: MCMC Versus Exponential Monte Carlo.

, , , and . AAAI, page 1431-1438. AAAI Press, (2018)

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Learning Chordal Markov Networks by Dynamic Programming., , and . NIPS, page 2357-2365. (2014)Counting Linear Extensions of Sparse Posets., , , and . IJCAI, page 603-609. IJCAI/AAAI Press, (2016)Counting Linear Extensions: Parameterizations by Treewidth., , , and . ESA, volume 57 of LIPIcs, page 39:1-39:18. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2016)AS-ASL: Algorithm Selection with Auto-sklearn., , , , and . OASC, volume 79 of Proceedings of Machine Learning Research, page 19-22. PMLR, (2017)Combinatorial Algorithms with Applications in Learning Graphical Models.. University of Helsinki, Finland, (2016)base-search.net (ftunivhelsihelda:oai:helda.helsinki.fi:10138/169278).Averaging of Decomposable Graphs by Dynamic Programming and Sampling., , and . UAI, page 415-424. AUAI Press, (2015)A Scalable Scheme for Counting Linear Extensions., , , and . IJCAI, page 5119-5125. ijcai.org, (2018)Predicting the Hardness of Learning Bayesian Networks., , , , and . AAAI, page 2460-2466. AAAI Press, (2014)Counting Linear Extensions in Practice: MCMC Versus Exponential Monte Carlo., , , and . AAAI, page 1431-1438. AAAI Press, (2018)A Faster Tree-Decomposition Based Algorithm for Counting Linear Extensions., , and . IPEC, volume 115 of LIPIcs, page 5:1-5:13. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2018)